All files of the document are available at: “C:/Users/praha/OneDrive - The Pennsylvania State University/PSU Research Work/Groundwater & Air Pollution/0_DC_GWAP/0_DC_GWAP”

A1. Data

A1(A). Load data

Using cleaned and compiled data for NDVI,GW, & AOD. Data is compiled for all possible states but for analysis, I am considering 17 states as listed in “State_list17”. For GWL, data for state of “Telangana” is missing. Also, data for the year Rajasthan in year 2004 is missing.

For Rajasthan 2004, mean of 2003 and 2005 is imputed as 2004 value for the trend plots and Synthetic Control as they need balanced panel. For regression analysis, Rajasthan:2004 is left “NA”. Use the pre-compiled data for the 14 states

#> [1] "AOD_Filter20_SC_05_13.csv"       "Dist_AT_AVG_Period_17States.csv"
#> [3] "DoY_RF_Fire_Data_PB_HR.xlsx"     "GW_Filter19_SC_05_13.csv"       
#> [5] "GW_Filter19_SC_05_13_RJ_imp.csv" "NDVI_Filter20_SC_05_13.csv"     
#> [7] "NDVI_Synth_07_30.csv"            "Synth_GW_05_13.csv"

Figure A1(A1). Gridded NDVI data

Figure A1(A2). Gridded AOD data availability for P1

Here the count provides number of records available in each periods of 41 days length. records include 2 satellite data surces so there shall be 41x2=82 records for each grid pixel for complete data availability. (Year 2002 for Perid 1 has only 1 satellite operational(Aqua Satellite was launched on May 4, 2002)).

Figure A1(A3). Gridded AOD data availability for P2

Figure A1(A4). Gridded Average AOD level in P1

Average AOD level is counted using all the available records for each grid pixel.

Figure A1(A5). Average AOD level in P2

A1(B). Summary Table

Summary for state level data (Treatment State and Time)
Table A1(B1): NDVI Summary Statistics
Post Tr_State Count Mean Min Max SD
1 0 0 105 0.42 0.15 0.73 0.11
2 0 1 14 0.31 0.24 0.40 0.05
3 1 0 120 0.42 0.16 0.71 0.11
4 1 1 16 0.27 0.20 0.38 0.05
Table A1(B2): Groundwater Level Summary Statistics
Post Tr_State Count Mean Min Max SD
1 0 0 98 7.88 4.55 16.92 2.71
2 0 1 14 10.01 9.22 11.07 0.43
3 1 0 112 7.70 4.54 16.48 2.56
4 1 1 16 10.58 8.56 11.77 0.98
Table A1(B3): AOD Level Summary Statistics
Post Tr_State Period Count Mean Min Max SD
1 0 0 0 120 0.33 0.19 0.58 0.09
2 0 0 1 105 0.34 0.14 0.80 0.13
3 0 1 0 16 0.42 0.34 0.54 0.06
4 0 1 1 14 0.73 0.63 0.93 0.09
5 1 0 0 105 0.39 0.20 0.63 0.09
6 1 0 1 120 0.47 0.18 1.02 0.16
7 1 1 0 14 0.46 0.36 0.61 0.07
8 1 1 1 16 0.86 0.64 1.09 0.12

A1(C). Analysis Data Plots

A2. Event Study

A2(A). Event Study Models for NDVI

A2(A1). NDVI_M1. Plotting NDVI TWFE Model w/o any control

A2(A2). NDVI_M2. Plotting NDVI TWFE Model w/ May and June RF control

A2(A3). NDVI_M3. Plotting NDVI TWFE Model w/ May and June RF, June Temp control

A2(A4). NDVI_M4. Plotting log(NDVI)-Linear Control TWFE Model w/ May and June RF, June Temp control

A2(A5). NDVI_M5. Plotting log(NDVI)-Log Control TWFE Model w/ May and June RF, June Temp control

(Used in the Paper)

A2(B). Event Study Models for GWL

A2(B1). GWL_M1. Plotting GWL TWFE Model w/o any control

A2(B2). GWL_M2. Plotting GWL TWFE Model w/ May RF control

A2(B3). GWL_M3. Plotting GWL TWFE Model w/ May RF & May TP control

A2(B4). GWL_M4. Plotting Log(GWL)-linear control TWFE Model w/ May RF & May TP control

A2(B5). GWL_M5. Plotting Log(GWL)-Log control TWFE Model w/ May RF & May TP control

(Used in the Paper)

A2(C). Event Study Models for AOD (DDD Models)

A2(C1). AOD_M1. Plotting AOD TWFE Model w/o any control

A2(C2). AOD_M2. Plotting AOD TWFE Model w/ P1+P2 RF

A2(C3). AOD_M3. Plotting AOD TWFE Model w/ P1+P2 RF & Temp

A2(C4). AOD_M4. Plotting Log(AOD)-linear control TWFE Model w/ P1+P2 RF & Temp

A2(C5). AOD_M5. Plotting Log(AOD)-Log control TWFE Model w/ P1+P2 RF & Temp

(Used in the Paper)

A3. Regressions (Log-Log Regressions)

A3(A). DiD Regression for Log(NDVI)

Log(NDVI) DiD Regression Table

Table A3(A): Log(GWL) DiD Regression (State-Level)
Log_NDVI
OLS felm
No FE Clt. Strd. Err State FE State+Year FE State+Year FE State+Year FE
(1) (2) (3) (4) (5) (6)
Post 0.006 0.006 0.006
(0.040) (0.006) (0.006) (0.000) (0.000) (0.000)
Tr_State -0.258*** -0.258**
(0.085) (0.127) (0.000) (0.000) (0.000) (0.000)
DiD -0.145 -0.145*** -0.145*** -0.145*** -0.136*** -0.136***
(0.117) (0.023) (0.023) (0.040) (0.040) (0.039)
Log_Rainfall_May 0.008 0.008
(0.009) (0.008)
Log_Rainfall_June 0.022* 0.023
(0.011) (0.017)
Log_Temp_June 0.218
(2.938)
Constant -0.918*** -0.918***
(0.029) (0.084)
State Fixed-Effect No No Yes Yes Yes Yes
Year Fixed-Effect No No No Yes Yes Yes
Clustered Std. Error State State State+Year State+Year State+Year
Control Factors Log RF(May&June) Log RF+TP(May&June)
Observations 255 255 255 255 255 255
R2 0.121 0.121 0.962 0.973 0.974 0.974
Adjusted R2 0.111 0.111 0.960 0.969 0.970 0.970
Residual Std. Error 0.300 (df = 251) 0.300 (df = 251) 0.064 (df = 236) 0.056 (df = 223) 0.055 (df = 221) 0.055 (df = 220)
F Statistic 11.568*** (df = 3; 251)
p<0.1; p<0.05; p<0.01

A3(B). DiD Regression for Log(GWL)

Log(GW) DiD Regression Table

Table A3(B): Log(GWL) DiD Regression (State-Level)
Log_GW_Level
OLS felm
No FE Clt. Strd. Err State FE State+Year FE State+Year FE State+Year FE
(1) (2) (3) (4) (5) (6)
Post -0.011 -0.011 -0.019
(0.039) (0.016) (0.015) (0.000) (0.000) (0.000)
Tr_State 0.296*** 0.296***
(0.079) (0.077) (0.000) (0.000) (0.000) (0.000)
DiD 0.063 0.063 0.071 0.071 0.071 0.070
(0.109) (0.050) (0.050) (0.051) (0.050) (0.052)
Log_Rainfall_May 0.002 0.001
(0.016) (0.017)
Log_Rainfall_June 0.0001 0.0002
(0.0002) (0.0002)
Log_Temp_May -1.093
(4.128)
Log_Temp_June 0.008
(0.015)
Constant 2.007*** 2.007***
(0.028) (0.077)
State Fixed-Effect No No Yes Yes Yes Yes
Year Fixed-Effect No No No Yes Yes Yes
Clustered Std. Error State State State+Year State+Year State+Year
Control Factors Log RF (May) Log RF+TP (May)
Observations 239 239 239 239 239 239
R2 0.137 0.137 0.927 0.939 0.939 0.939
Adjusted R2 0.126 0.126 0.922 0.930 0.930 0.929
Residual Std. Error 0.278 (df = 235) 0.278 (df = 235) 0.083 (df = 221) 0.079 (df = 208) 0.079 (df = 206) 0.079 (df = 204)
F Statistic 12.406*** (df = 3; 235)
p<0.1; p<0.05; p<0.01

A3(C). DDD Regression for Log(AOD)

Log(AOD) DDD Regression Table

Table A3(C1): Log(AOD) Period-wise Separate DiD Regression (State-Level)
Dependent variable:
Log_AOD
Control Period Treatment Period
(1) (2) (3)
Tr_State
(0.000) (0.000) (0.000)
Post -0.067**
(0.000) (0.000) (0.030)
Period 0.107
(0.091)
DiD -0.043 -0.107* -0.104*
(0.077) (0.058) (0.056)
DDD -0.038
(0.042)
Log_Rainfall_P -0.041 0.032* -0.037**
(0.025) (0.017) (0.014)
Log_Temp_P 19.038 25.032** 3.821
(11.851) (9.540) (5.803)
Post:Period 0.141***
(0.043)
Tr_State:Period 0.425***
(0.063)
State Fixed-Effect Yes Yes
Year Fixed-Effect Yes Yes
Clustered Std. Error State+Year State+Year
Control Factors Log RF+TP (P1&P2) Log RF+TP (P1&P2)
Observations 255 255 510
R2 0.763 0.900 0.756
Adjusted R2 0.728 0.885 0.736
Residual Std. Error 0.140 (df = 221) 0.142 (df = 221) 0.184 (df = 471)
p<0.1; p<0.05; p<0.01
Table A3(C2): Log(AOD) Period-wise Separate DDD Regression (State-Level)
Log_AOD
No FE Clt. Strd. Err State FE State+Year FE State+Year FE State+Year FE State+Year FE
(1) (2) (3) (4) (5) (6) (7)
Tr_State 0.291*** 0.291***
(0.079) (0.056) (0.000) (0.000) (0.000) (0.000) (0.000)
Post 0.187*** 0.187*** 0.187*** -0.089* -0.065* -0.067** -0.066**
(0.037) (0.037) (0.037) (0.046) (0.033) (0.030) (0.030)
Period 0.023 0.023 0.023 0.035 0.058 0.107 0.090
(0.037) (0.055) (0.055) (0.057) (0.053) (0.091) (0.089)
DiD -0.125 -0.125*** -0.125*** -0.148** -0.117** -0.104* -0.098
(0.108) (0.036) (0.036) (0.060) (0.054) (0.056) (0.060)
DDD -0.018 -0.018 -0.018 0.006 -0.031 -0.038 -0.037
(0.153) (0.016) (0.016) (0.045) (0.040) (0.042) (0.047)
Log_Rainfall_P -0.040** -0.037** -0.039**
(0.014) (0.014) (0.013)
Log_Temp_P 3.821 3.269
(5.803) (5.674)
Post:Period 0.121** 0.121*** 0.121*** 0.132** 0.142*** 0.141*** 0.151***
(0.053) (0.018) (0.018) (0.059) (0.046) (0.043) (0.044)
Tr_State:Period 0.508*** 0.508*** 0.508*** 0.496*** 0.426*** 0.425*** 0.422***
(0.112) (0.063) (0.062) (0.073) (0.061) (0.063) (0.067)
Constant -1.149*** -1.149***
(0.025) (0.056)
State Fixed-Effect No No Yes Yes Yes Yes Yes
Year Fixed-Effect No No No Yes Yes Yes Yes
Clustered Std. Error State State State+Year State+Year State+Year State+Year
Control Factors Log RF (P1&P2) Log RF+TP (P1&P2) Log RF+TP (P1&P2)
Observations 510 510 510 510 510 510 476
R2 0.396 0.396 0.702 0.742 0.755 0.756 0.746
Adjusted R2 0.387 0.387 0.689 0.723 0.736 0.736 0.724
Residual Std. Error 0.281 (df = 502) 0.281 (df = 502) 0.200 (df = 487) 0.189 (df = 473) 0.184 (df = 472) 0.184 (df = 471) 0.185 (df = 438)
p<0.1; p<0.05; p<0.01

A4. Robustness Checks: Air Pollution Spillover to U.P. and Bihar

Here I am excluding U.P. and Bihar states from the control group due to spillover of pollutant transfer. The Event study and regression is re-estimated with 2 less states for control.

#>  [1] "Andhra Pradesh" "Bihar"          "Chhattisgarh"   "Gujarat"       
#>  [5] "Haryana"        "Jharkhand"      "Karnataka"      "Kerala"        
#>  [9] "Madhya Pradesh" "Maharashtra"    "Orissa"         "Punjab"        
#> [13] "Rajasthan"      "Tamilnadu"      "Uttar Pradesh"  "West Bengal"

A4(A). Event Study for 12 States

A4(A1). NDVI_M5. Plotting Log(NDVI)-Log Control TWFE Model w/ May and June RF, June Temp control (12 States)

A4(A2). GWL_M5. Plotting Log(GWL)-Log control TWFE Model w/ May RF & May TP control (12 States)

A4(A3). AOD_M5. Plotting Log(AOD)-Log control TWFE Model w/ P1+P2 RF & Temp 12 (12 States)

A4(B). DiD and DDD Regression Estimates for 12 States

A4(B1). Log(NDVI) DiD Regression Table (W/ Robustness Check)

Table A4(B1): Log(NDVI) (Month of June)
Log_NDVI
OLS felm
No FE Clt. Strd. Err State FE State+Year FE State+Year FE State+Year FE State+Year FE
(1) (2) (3) (4) (5) (6) (7)
DiD -0.145 -0.145*** -0.145*** -0.145*** -0.136*** -0.136*** -0.136***
(0.117) (0.023) (0.023) (0.040) (0.040) (0.039) (0.041)
State Fixed-Effect No No Yes Yes Yes Yes Yes
Year Fixed-Effect No No No Yes Yes Yes Yes
Clustered Std. Error State State State+Year State+Year State+Year State+Year
Control Factors Log RF(May&June) Log RF+TP(May&June) Log RF+TP(May&June)
Control States 15 15 15 15 15 15 13
Observations 255 255 255 255 255 255 225
R2 0.121 0.121 0.962 0.973 0.974 0.974 0.974
Adjusted R2 0.111 0.111 0.960 0.969 0.970 0.970 0.970
Residual Std. Error 0.300 (df = 251) 0.300 (df = 251) 0.064 (df = 236) 0.056 (df = 223) 0.055 (df = 221) 0.055 (df = 220) 0.058 (df = 192)
F Statistic 11.568*** (df = 3; 251)
p<0.1; p<0.05; p<0.01

A4(B2). Log(GW) DiD Regression Table (W/ Robustness Check)

Table A4(B2): Log(GWL) (Pre-Monsoon)
Log_GW_Level
OLS felm
No FE Clt. Strd. Err State FE State+Year FE State+Year FE State+Year FE State+Year FE
(1) (2) (3) (4) (5) (6) (7)
DiD 0.063 0.063 0.071 0.071 0.071 0.070 0.083
(0.109) (0.050) (0.050) (0.051) (0.050) (0.052) (0.052)
State Fixed-Effect No No Yes Yes Yes Yes Yes
Year Fixed-Effect No No No Yes Yes Yes Yes
Clustered Std. Error State State State+Year State+Year State+Year State+Year
Control Factors Log RF (May) Log RF+TP (May) Log RF+TP (May)
Control States 14 14 14 14 14 14 12
Observations 239 239 239 239 239 239 209
R2 0.137 0.137 0.927 0.939 0.939 0.939 0.942
Adjusted R2 0.126 0.126 0.922 0.930 0.930 0.929 0.932
Residual Std. Error 0.278 (df = 235) 0.278 (df = 235) 0.083 (df = 221) 0.079 (df = 208) 0.079 (df = 206) 0.079 (df = 204) 0.078 (df = 178)
F Statistic 12.406*** (df = 3; 235)
p<0.1; p<0.05; p<0.01

A4(B3). Log(AOD) DDD Regression Table (W/ Robustness Check)

Table A4(B3): Log(AOD) (DDD)
Log_AOD
No FE Clt. Strd. Err State FE State+Year FE State+Year FE State+Year FE State+Year FE
(1) (2) (3) (4) (5) (6) (7)
DDD -0.018 -0.018 -0.018 0.006 -0.031 -0.038 -0.027
(0.153) (0.016) (0.016) (0.045) (0.040) (0.042) (0.048)
State Fixed-Effect No No Yes Yes Yes Yes Yes
Year Fixed-Effect No No No Yes Yes Yes Yes
Clustered Std. Error State State State+Year State+Year State+Year State+Year
Control Factors Log RF (P1&P2) Log RF+TP (P1&P2) Log RF+TP (P1&P2)
Control States 15 15 15 15 15 15 13
Observations 510 510 510 510 510 510 450
R2 0.396 0.396 0.702 0.742 0.755 0.756 0.748
Adjusted R2 0.387 0.387 0.689 0.723 0.736 0.736 0.726
Residual Std. Error 0.281 (df = 502) 0.281 (df = 502) 0.200 (df = 487) 0.189 (df = 473) 0.184 (df = 472) 0.184 (df = 471) 0.178 (df = 413)
p<0.1; p<0.05; p<0.01

A4(C). Exporting the LaTeX tables (Make Changes)

A5. Synthetic Control for GW Level DiD Estimation

(1)Combined Punjab and Haryana for treatment (2)Andhra Pradesh and Telangana data is pro-rated for synthetic control pre-2014 period as the erstwhile state of Andhra Pradesh was bifurcated into two states in 2014.

A5(A). Synthetic Control for Groundwater

P.S: Unfortunately the synthetic control plot commands are not conducive for making colorful illustrations like ggplot2. Hence, the B/W charts are used in for synthetic control analysis.

A5(B). Backdating Synthetic Control Prediction to 2007

A5(C). Backdating Synthetic Control Prediction to 2006

A5(D). Compiling Weights from 5(A-C)

Table 5(D1): Control States Weights for Synthetic Control
Control State Weights Weights (Backdating 1 Yr) Weights (Backdating 2 Yr)
Andhra Pradesh 0.006 0.011 0.006
Bihar 0.003 0.007 0.004
Chhattisgarh 0.011 0.010 0.014
Gujarat 0.008 0.022 0.008
Jharkhand 0.006 0.010 0.006
Karnataka 0.005 0.009 0.004
Kerala 0.004 0.006 0.003
Madhya Pradesh 0.017 0.021 0.016
Maharashtra 0.003 0.011 0.004
Orissa 0.003 0.007 0.005
Rajasthan 0.317 0.307 0.318
Tamilnadu 0.005 0.010 0.006
Uttar Pradesh 0.611 0.562 0.604
West Bengal 0.003 0.007 0.003
Table 5(D2): Predictor Summary
Treated Synthetic Sample Mean Treated (BD1) Synthetic (BD1) Sample Mean (BD1) Treated (BD2) Synthetic (BD2) Sample Mean (BD2)
Rainfall(May) 22.491 18.555 65.399 18.712 19.033 66.944 19.115 15.918 69.218
Temperature (May) 305.286 306.039 304.282 305.489 306.079 304.317 305.481 306.240 304.311
Rainfall(June) 79.172 133.970 216.791 59.179 114.993 207.459 53.509 109.260 195.073
Temperature (June) 305.703 305.438 302.716 306.096 305.648 302.929 306.092 305.816 302.963
GW_Level 10.018 10.016 7.879 9.927 9.931 7.943 9.965 9.969 7.975
Population 49299.000 135469.873 67699.527 49299.000 129714.016 67699.527 49299.000 134627.396 67699.527
SoD 128.171 85.682 53.357 128.171 84.190 53.357 128.171 85.491 53.357
GSDP 22908838.400 24331914.513 19189419.314 21963330.500 23161662.083 18413476.661 20971307.667 22432273.047 17529701.143
AgGDP 6828382.000 6737116.319 3514804.529 6221277.750 5946515.113 3271998.750 5699963.333 5781119.568 3019477.643
Rice Cultivation Area(Kharif) 3760.930 3703.427 1442.370 3714.620 3441.098 1434.119 3725.303 3664.033 1435.945
Table 5(D3): Predictor Variable Weights
Weights Weights (Backdating 1 Yr) Weights (Backdating 2 Yr)
Rainfall(May) 0.013 0.041 0.065
Temperature (May) 0 0.012 0
Rainfall(June) 0 0 0
Temperature (June) 0.03 0.103 0.071
GW_Level 0.806 0.79 0.766
Population 0 0.001 0
SoD 0 0 0
GSDP 0.045 0.009 0.017
AgGDP 0.042 0.044 0.025
Rice Cultivation Area(Kharif) 0.063 0 0.056

A6. District Level Analysis for AOD

Table A6(A): AOD Level Summary Statistics (District-Level Data)
Post Tr_State Period Count Mean Min Max SD
1 0 0 0 3544 0.33 0.08 1.13 0.11
2 0 0 1 3101 0.39 0.08 1.16 0.18
3 0 1 0 328 0.42 0.25 0.57 0.07
4 0 1 1 287 0.71 0.30 1.22 0.17
5 1 0 0 3101 0.39 0.11 0.85 0.12
6 1 0 1 3544 0.52 0.07 1.43 0.22
7 1 1 0 287 0.45 0.25 0.68 0.08
8 1 1 1 328 0.84 0.28 1.50 0.23

Call: felm(formula = Log_AOD ~ relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”) + Period + Tr_State * Post + Tr_State * Period + Period * Post + Log_Rainfall + Log_Temp | State + Year | 0 | State + Year, data = dt)

Residuals: Min 1Q Median 3Q Max -1.40137 -0.16220 0.00899 0.16992 1.48036

Coefficients: Estimate relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-7 1.234e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 -1.521e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 1.041e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 3.627e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 2.100e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 -5.168e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 9.661e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 5.881e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 -2.581e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 8.337e-03 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 -5.708e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 -8.825e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 -2.833e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 -4.702e-04 Period 3.053e-01 Tr_State NaN Post -1.277e-01 Log_Rainfall -9.470e-03 Log_Temp 1.167e+00 Tr_State:Post -4.235e-02 Period:Tr_State 4.087e-01 Period:Post 1.548e-01 Cluster s.e. relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-7 4.544e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 5.355e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 4.824e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 7.698e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 2.139e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 2.901e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 6.988e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 4.401e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 3.622e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 6.677e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 4.106e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 2.684e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 2.220e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 7.370e-02 Period 6.552e-02 Tr_State 3.878e-18 Post 3.013e-02 Log_Rainfall 1.152e-02 Log_Temp 3.593e-01 Tr_State:Post 4.396e-02 Period:Tr_State 6.789e-02 Period:Post 3.906e-02 t value relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-7 2.716 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 -0.284 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 2.157 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 0.471 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 9.818 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 -1.782 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 1.383 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 1.336 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 -7.124 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 0.125 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 -1.390 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 -3.288 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 -1.277 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 -0.006 Period 4.660 Tr_State NaN Post -4.237 Log_Rainfall -0.822 Log_Temp 3.248 Tr_State:Post -0.963 Period:Tr_State 6.020 Period:Post 3.963 Pr(>|t|) relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-7 0.016721 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 0.780535 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 0.048844 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 0.644822 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 1.17e-07 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 0.096524 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 0.188473 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 0.202758 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 5.14e-06 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 0.902405 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 0.186177 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 0.005386 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 0.222540 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 0.995000 Period 0.000368 Tr_State NaN Post 0.000829 Log_Rainfall 0.424707 Log_Temp 0.005837 Tr_State:Post 0.351700 Period:Tr_State 3.15e-05 Period:Post 0.001415

relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-7 *
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 *
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 relevel(factor(Time_To_Treatment Tr_State * Period), ref = “-1”)-2 .
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 relevel(factor(Time_To_Treatment Tr_State * Period), ref = “-1”)3
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 ** relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7
Period Tr_State
Post Log_Rainfall
Log_Temp ** Tr_State:Post
Period:Tr_State * Period:Post — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Residual standard error: 0.2728 on 14359 degrees of freedom Multiple R-squared(full model): 0.6041 Adjusted R-squared: 0.6027 Multiple R-squared(proj model): 0.2261 Adjusted R-squared: 0.2234 F-statistic(full model, iid):438.3 on 50 and 14359 DF, p-value: < 2.2e-16 F-statistic(proj model): 56.21 on 22 and 14 DF, p-value: 3.882e-10

Table A6(B): Log(AOD) Period-wise DiD Regression
Dependent variable:
Log_AOD
Control Period Treatment Period
(1) (2)
Tr_State
(0.000) (0.000)
Post
(0.000) (0.000)
Post_Tr_State -0.057 -0.120**
(0.052) (0.051)
Log_Rainfall 0.006 0.015
(0.007) (0.009)
Log_Temp 1.046** 2.157***
(0.440) (0.512)
State Fixed-Effect Yes Yes
Year Fixed-Effect Yes Yes
Clustered Std. Error State+Year State+Year
Control Factors Log RF+TP (P1&P2) Log RF+TP (P1&P2)
Observations 7,239 7,171
R2 0.537 0.740
Adjusted R2 0.535 0.739
Residual Std. Error 0.234 (df = 7206) 0.246 (df = 7138)
p<0.1; p<0.05; p<0.01
Table A6 (C) :Log(AOD) (DDD) State & District Level Analysis (With Spillover Effect Control)
Log_AOD
State-Level State-Level Dist-Level Dist-Level
(1) (2) (3) (4)
DDD -0.038 -0.027 -0.092** -0.084*
(0.042) (0.048) (0.034) (0.042)
Unit of Analysis State State Dist Dist
State Fixed-Effect Yes Yes Yes Yes
Year Fixed-Effect Yes Yes Yes Yes
Clustered Std. Error State+Year State+Year State+Year State+Year
Control Factors Log RF+TP (P1&P2) Log RF+TP (P1&P2) Log RF+TP (P1&P2) Log RF+TP (P1&P2)
Control States 15 13 15 13
Observations 510 450 14,410 11,168
R2 0.756 0.748 0.602 0.550
Adjusted R2 0.736 0.726 0.601 0.549
Residual Std. Error 0.184 (df = 471) 0.178 (df = 413) 0.273 (df = 14372) 0.271 (df = 11132)
p<0.1; p<0.05; p<0.01

A7. Dropping 2002 from AOD Data

Call: felm(formula = Log_AOD ~ relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”) + Period + Tr_State * Post + Tr_State * Period + Period * Post + Log_Rainfall + Log_Temp | State + Year | 0 | State + Year, data = dt)

Residuals: Min 1Q Median 3Q Max -1.39306 -0.16035 0.00485 0.16592 1.50446

Coefficients: Estimate relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 -1.844e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 9.388e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 2.622e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 2.104e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 -4.063e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 6.966e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 2.941e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 -2.600e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 -2.144e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 -9.108e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 -1.165e-01 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 -5.620e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 1.161e-02 Period 3.279e-01 Tr_State NaN Post -1.293e-01 Log_Rainfall -1.953e-02 Log_Temp 4.415e-02 Tr_State:Post -2.635e-02 Period:Tr_State 3.405e-01 Period:Post 1.714e-01 Cluster s.e. relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 5.161e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 3.555e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 7.545e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 2.370e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 2.038e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 6.256e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 3.901e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 3.427e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 5.760e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 3.452e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 2.983e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 2.153e-02 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 5.344e-02 Period 7.417e-02 Tr_State 1.340e-17 Post 2.577e-02 Log_Rainfall 1.367e-02 Log_Temp 1.564e-02 Tr_State:Post 4.506e-02 Period:Tr_State 6.822e-02 Period:Post 3.724e-02 t value relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 -0.357 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 2.641 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 0.348 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 8.877 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 -1.994 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 1.113 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 0.754 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 -7.587 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 -0.372 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 -2.638 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 -3.905 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 -2.610 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 0.217 Period 4.421 Tr_State NaN Post -5.017 Log_Rainfall -1.428 Log_Temp 2.824 Tr_State:Post -0.585 Period:Tr_State 4.991 Period:Post 4.603 Pr(>|t|) relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6 0.726587 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 0.020363 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4 0.733720 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 7.03e-07 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-2 0.067531 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0 0.285672 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1 0.464327 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 3.97e-06 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)3 0.715751 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 0.020459 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 0.001809 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 0.021588 relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7 0.831439 Period 0.000691 Tr_State NaN Post 0.000236 Log_Rainfall 0.176825 Log_Temp 0.014359 Tr_State:Post 0.568670 Period:Tr_State 0.000247 Period:Post 0.000495

relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-6
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-5 *
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-4
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)-3 relevel(factor(Time_To_Treatment Tr_State * Period), ref = “-1”)-2 .
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)0
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)1
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)2 relevel(factor(Time_To_Treatment Tr_State * Period), ref = “-1”)3
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)4 *
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)5 ** relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)6 *
relevel(factor(Time_To_Treatment * Tr_State * Period), ref = “-1”)7
Period Tr_State
Post Log_Rainfall
Log_Temp *
Tr_State:Post
Period:Tr_State Period:Post — Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Residual standard error: 0.269 on 13503 degrees of freedom Multiple R-squared(full model): 0.6096 Adjusted R-squared: 0.6082 Multiple R-squared(proj model): 0.2522 Adjusted R-squared: 0.2496 F-statistic(full model, iid):439.3 on 48 and 13503 DF, p-value: < 2.2e-16 F-statistic(proj model): 221.9 on 21 and 13 DF, p-value: 2.355e-13

Table A7: Log(AOD) (DDD) (Dropping Year 2002)
Log_AOD
State-Level State-Level Dist-Level Dist-Level
(1) (2) (3) (4)
DDD -0.037 -0.028 -0.099** -0.093**
(0.047) (0.056) (0.035) (0.041)
Unit of Analysis State State Dist Dist
State Fixed-Effect Yes Yes Yes Yes
Year Fixed-Effect Yes Yes Yes Yes
Clustered Std. Error State+Year State+Year State+Year State+Year
Control Factors Log RF+TP (P1&P2) Log RF+TP (P1&P2) Log RF+TP (P1&P2) Log RF+TP (P1&P2)
Control States 15 13 15 13
Observations 476 420 13,552 10,500
R2 0.746 0.738 0.608 0.548
Adjusted R2 0.724 0.714 0.607 0.546
Residual Std. Error 0.185 (df = 438) 0.179 (df = 384) 0.270 (df = 13515) 0.269 (df = 10465)
p<0.1; p<0.05; p<0.01

A8. NDVI Data Synthetic Control

[1] “State” “St_Code” “Year”
[4] “NDVI” “Tr_State” “Post”
[7] “DiD” “Rainfall_June” “RainyDays_June”
[10] “Rainfall_May” “RainyDays_May” “Rainfall_P1”
[13] “RainyDays_P1” “Rainfall_P2” “RainyDays_P2”
[16] “Temp_June” “Temp_May” “Temp_P1”
[19] “Temp_P2” “Time_To_Treatment” “Log_NDVI”
[22] “Log_Rainfall_June” “Log_Rainfall_May” “Log_Temp_June”
[25] “Log_Temp_May” “GSDP” “AgGDP”
[28] “Population” “SoD” “RiceT”
[31] “RiceK”

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001572315

solution.v: 0.05420307 0.1466799 2.7397e-06 0.08877862 0.1472921 0.06709368 0.1221464 0.08372835 4.34372e-05 0.2900317

solution.w: 0.0009475729 0.0001339082 0.04773945 0.0005710098 7.40704e-05 0.0002925387 4.2997e-06 0.0007361399 0.0001684055 0.0001334264 0.3693999 0.1745274 0.0008981431 0.4043738

[1] “tab.pred” “tab.v” “tab.w” “tab.loss” Treated Synthetic Sample Mean Rainfall_May 22.491 29.592 56.517 Temp_May 305.286 305.731 304.562 Rainfall_June 79.172 115.619 198.549 Temp_June 305.703 305.009 302.823 special.NDVI.2002.2008 0.315 0.315 0.411 special.Population.2006 49299.000 110158.343 63968.964 special.SoD.2004 128.171 90.475 54.429 special.GSDP.2004.2008 22908838.400 22895687.993 18314617.814 special.AgGDP.2004.2008 6828382.000 5500046.323 3318134.971 special.RiceK.2004.2008 3760.930 2921.604 1484.433 v.weights Rainfall_May 0.054
Temp_May 0.147
Rainfall_June 0
Temp_June 0.089
special.NDVI.2002.2008 0.147
special.Population.2006 0.067
special.SoD.2004 0.122
special.GSDP.2004.2008 0.084
special.AgGDP.2004.2008 0
special.RiceK.2004.2008 0.29
Loss W Loss V [1,] 0.3951706 0.0001572315 w.weight 1 9.475729e-04 2 1.339082e-04 3 4.773945e-02 4 5.710098e-04 5 7.407037e-05 6 2.925387e-04 7 4.299665e-06 8 7.361399e-04 9 1.684055e-04 10 1.334264e-04 11 3.693999e-01 12 1.745274e-01 13 8.981431e-04 14 4.043738e-01 [1] 0.04773945 0.36939991 0.17452741 0.40437377

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.000390147

solution.v: 0.01424146 1.3178e-06 0.0406692 0.04075915 0.7698587 0.02155787 7.5e-08 0.003651695 0.1089594 0.0003011593

solution.w: 0.02400693 0.04045245 0.07592969 0.03421162 0.05955377 0.02673113 0.06009503 0.03623124 0.04782634 0.06346042 0.2973092 0.1017849 0.132407

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001618406

solution.v: 0.01178876 0 0.009809927 0 0.9079972 0.007979565 0.01841646 0.01692064 0.02399703 0.00309043

solution.w: 0.01229246 0.0004090846 0.03072638 0.5803956 0.03092839 0.05587657 0.03815362 0.01259408 1.52531e-05 0.06694794 0.009625234 0.009025644 0.1530098

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.000157143

solution.v: 0.04767206 0.002689464 0.03902223 3.22444e-05 0.7763535 0.02961256 0.02967596 0.03168709 0.0377995 0.005455364

solution.w: 0.009866235 0.003891589 0.002651938 0.7081778 0.003677174 0.02473333 0.1292526 0.003499013 0.06803382 0.0006070617 0.003441471 0.03798734 0.00418047

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 4.773238e-06

solution.v: 0.2241937 0.001037908 0.0002311095 2.66916e-05 0.3784413 0.1803174 5.2926e-06 1.0787e-06 0.1464005 0.06934498

solution.w: 0.0394239 0.01577546 0.02529356 0.02055563 0.02486745 0.000365092 0.04392392 0.03362842 0.02079646 0.6966788 0.01596579 0.03245633 0.03026911

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 9.740222e-05

solution.v: 5.162e-07 0.00916579 0.05583244 0.01994815 0.8149031 2.968e-07 1.872e-07 0.09958072 0.0001469166 0.0004218885

solution.w: 0.03301892 0.1039138 0.4539724 0.01225714 0.03023987 0.1235881 0.03424756 0.008330819 0.1070278 0.01184459 0.02333575 0.04263157 0.01559145

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001903824

solution.v: 1.8229e-06 0.00300845 6.38e-08 0.004840446 0.8045127 0.01537627 0.03973682 0.03551923 0.02903578 0.06796838

solution.w: 0.03076806 0.0222533 0.02312388 0.4124712 0.01909078 0.1868047 0.02084109 5.60705e-05 0.01768754 0.0379909 0.1814683 0.01695653 0.0304874

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.03152589

solution.v: 0.01739718 0.0007824561 0.01519618 0.0007706755 0.9220628 0.01309564 0.008844571 0.001242076 0.01921086 0.001397514

solution.w: 7.91066e-05 0.0001233193 0.0001013564 1.36947e-05 2.7049e-06 8.04897e-05 5.1231e-05 6.20843e-05 0.9990416 5.041e-07 0.0002774567 0.0001081957 5.81933e-05

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 8.449551e-05

solution.v: 0.1420808 0.03128715 0.02895621 0.009091418 0.6612678 0.001435262 0.02187938 0.07764148 0.0007896677 0.0255708

solution.w: 0.03806093 0.02747056 0.3354178 0.06525281 0.04069215 0.01879509 0.002604674 0.0376877 0.04945733 0.2249116 0.01694996 0.1039098 0.03878912

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 5.707544e-05

solution.v: 0.123968 0.001106227 0.01519019 0.002038121 0.8367914 0.002641329 0.005204254 0.0001842452 0.01237232 0.0005038329

solution.w: 0.02476911 0.0457056 0.09230854 0.1878266 0.05738182 0.03754688 0.01189864 0.09771222 0.06364703 3.79922e-05 0.01519005 0.03064332 0.3353317

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0002553796

solution.v: 7.9096e-06 0.002290694 0.02704248 0.006565434 0.8065916 0.1035186 0.0085905 1.62272e-05 0.04376386 0.001612773

solution.w: 0.05445954 0.03982485 0.2447029 0.01022447 0.2027556 0.02561016 0.2300446 0.03233548 0.02431132 0.002405432 0.04990447 0.07045275 0.01296765

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.005553722

solution.v: 0.001226548 2.98123e-05 0.01744958 1.52e-08 0.9138121 1.70819e-05 0.05726646 2.82814e-05 0.009380906 0.0007892008

solution.w: 5.3616e-06 4.555e-06 3.278e-06 0.9999391 3.0709e-06 7.9267e-06 6.53e-08 7.1418e-06 6.586e-06 2.5415e-06 5.9451e-06 5.318e-06 9.0884e-06

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.000477725

solution.v: 0.4171317 0.263623 0.02461644 0.0002058697 0.2485497 0.02658308 0.003794997 0.0004596895 0.0146031 0.000432454

solution.w: 0.07649267 0.06962155 0.04375978 0.05577287 0.06610306 0.1588538 0.2329503 0.04037165 0.05041233 0.06050089 0.03815627 0.04880918 0.05816893

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0002357812

solution.v: 0.002702115 0.03383094 1.25e-08 0.0004617649 0.9239821 0.02457356 4.561e-07 0.01240352 0.002045412 1.757e-07

solution.w: 0.04101174 0.03540497 0.5468453 0.02927511 0.04253958 0.02961962 0.02071286 0.05845103 0.03798982 0.06100433 0.03113061 0.03433458 0.03167972

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 4.060474e-05

solution.v: 0.031969 0.03960776 0.253923 0.00146535 0.1293159 0.03609445 0.07234974 0.2454709 0.1192615 0.07054243

solution.w: 0.1548312 0.01385767 0.01325612 0.06521354 0.0005138363 0.03248193 0.002116777 0.1021564 0.4545884 0.005456847 0.0663869 0.07832924 0.01080214

Backdating for NDVI [1] “State” “St_Code” “Year”
[4] “NDVI” “Tr_State” “Post”
[7] “DiD” “Rainfall_June” “RainyDays_June”
[10] “Rainfall_May” “RainyDays_May” “Rainfall_P1”
[13] “RainyDays_P1” “Rainfall_P2” “RainyDays_P2”
[16] “Temp_June” “Temp_May” “Temp_P1”
[19] “Temp_P2” “Time_To_Treatment” “Log_NDVI”
[22] “Log_Rainfall_June” “Log_Rainfall_May” “Log_Temp_June”
[25] “Log_Temp_May” “GSDP” “AgGDP”
[28] “Population” “SoD” “RiceT”
[31] “RiceK”

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001834653

solution.v: 0.01474062 0.01221754 0.0007253887 0.005711438 0.9342627 0.003849763 0.008196921 0.0005096298 0.0001520995 0.01963388

solution.w: 0.001306744 0.0003533698 0.02699816 0.0009776205 0.0006563009 0.0002835766 8.6113e-06 0.001460926 0.0004509492 0.0006571908 0.359741 0.1704884 0.002327699 0.4342894

[1] “tab.pred” “tab.v” “tab.w” “tab.loss” Treated Synthetic Sample Mean Rainfall_May 18.712 28.177 58.400 Temp_May 305.489 305.840 304.593 Rainfall_June 59.179 98.007 190.777 Temp_June 306.096 305.360 303.031 special.NDVI.2002.2007 0.314 0.314 0.413 special.Population.2006 49299.000 114499.714 63968.964 special.SoD.2004 128.171 90.807 54.429 special.GSDP.2004.2007 21963330.500 22588919.021 17542022.071 special.AgGDP.2004.2007 6221277.750 5236069.497 3076591.411 special.RiceK.2004.2007 3714.620 2987.381 1473.604 v.weights Rainfall_May 0.015
Temp_May 0.012
Rainfall_June 0.001
Temp_June 0.006
special.NDVI.2002.2007 0.934
special.Population.2006 0.004
special.SoD.2004 0.008
special.GSDP.2004.2007 0.001
special.AgGDP.2004.2007 0
special.RiceK.2004.2007 0.02
Loss W Loss V [1,] 0.0251982 0.0001834653 w.weight 1 1.306744e-03 2 3.533698e-04 3 2.699816e-02 4 9.776205e-04 5 6.563009e-04 6 2.835766e-04 7 8.611303e-06 8 1.460926e-03 9 4.509492e-04 10 6.571908e-04 11 3.597410e-01 12 1.704884e-01 13 2.327699e-03 14 4.342894e-01 [1] 0.02699816 0.35974097 0.17048844 0.43428944

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0004297768

solution.v: 0.01539953 3.00135e-05 0.06091758 0.03476192 0.8409411 0.0003727625 1.4589e-06 0.006448066 0.04048974 0.0006378567

solution.w: 0.0388082 0.04290803 0.05707911 0.04115846 0.0552999 0.0240503 0.0596887 0.03392734 0.0445168 0.06265216 0.323367 0.06076371 0.1557802

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001902658

solution.v: 0.007341279 0.0006134711 0.007741028 0.007456296 0.7032537 0.03702132 0.07225765 0.08477568 0.05411017 0.02542943

solution.w: 0.01513672 0.004434681 0.01717301 0.6066838 0.02356524 0.02788459 0.05036396 0.009915665 5.11358e-05 0.05360277 0.01401906 0.01924931 0.1579189

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001672026

solution.v: 0.01653482 2.018e-07 0.01875611 0.0003036871 0.9111141 0.01565113 0.006228153 0.003875076 0.02750926 2.74454e-05

solution.w: 0.01439435 0.01123235 0.01098625 0.8141388 0.0116746 2.46008e-05 0.01339223 0.01115662 0.06236638 0.009857567 0.0125107 0.01902463 0.009240918

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 4.156468e-06

solution.v: 0.04313074 0.0004506939 0.0001049299 0.000275774 0.9200634 0.02686096 4.04389e-05 5.07508e-05 0.005338803 0.003683523

solution.w: 0.03386124 0.01921729 0.02924747 0.02290753 0.02716177 0.0002956066 0.04375855 0.03064474 0.01919467 0.7016937 0.01759272 0.03108268 0.02334203

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 7.187783e-05

solution.v: 0.05163404 0.01330034 0.1156328 0.0136701 0.4926305 0.0003725365 0.0001535593 0.1841378 0.1219092 0.006559062

solution.w: 0.02131204 0.07202588 0.5126119 0.01039037 0.02387305 0.09315691 0.02617816 0.0007220725 0.1333293 0.01711536 0.01931237 0.069424 0.0005480166

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001945098

solution.v: 8.6855e-06 0.03557503 4.24509e-05 7.02265e-05 0.8663435 0.0002075417 0.0618592 0.02012141 0.0157718 1.192e-07

solution.w: 0.0360906 0.0369568 0.02362653 0.3598381 0.03171238 0.1012647 0.02569943 0.01928791 0.02582434 5.20616e-05 0.2914445 0.0219341 0.02626672

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.03201685

solution.v: 0.02611704 0.00101616 0.01610771 0.0006062854 0.9083837 0.01579127 1.0548e-06 0.007111219 0.02110603 0.003759487

solution.w: 6.85955e-05 0.0001040305 7.19489e-05 1.13972e-05 2.243e-06 7.05831e-05 4.12317e-05 5.57236e-05 0.9992595 4.241e-07 0.0001832766 8.40577e-05 4.68945e-05

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 8.717209e-05

solution.v: 0.1044679 0.03834056 5.71781e-05 0.002362734 0.7387023 0.008676266 0.03001521 0.05585324 0.01985206 0.001672564

solution.w: 0.04536292 0.03764575 0.2893962 0.06226185 0.04302019 0.01966552 0.003637495 0.04092295 0.05527895 0.2169319 0.01615411 0.09189015 0.07783137

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 6.191272e-05

solution.v: 0.0975725 0.01435528 0.001754693 9.7274e-05 0.8812646 0.00178515 0.001028101 4.6228e-06 0.002033567 0.0001042607

solution.w: 0.04890979 0.04757251 0.06250943 0.1468502 0.04756994 0.04269754 0.01506655 0.1243117 0.04938156 0.02300381 0.03436916 0.05627926 0.3014783

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0002704445

solution.v: 1.56826e-05 1.04542e-05 0.002618007 0.004978862 0.6828858 0.2301376 0.008075333 0.0002770502 0.02214933 0.04885185

solution.w: 0.04655873 0.03601251 0.0883803 0.01499596 0.3952575 0.02947458 0.2389687 0.03176546 0.02405409 0.006533118 0.03551285 0.04502319 0.007462872

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.005753193

solution.v: 2.3131e-05 6.9711e-06 3.37624e-05 0.0005944112 0.5065161 0.003247884 0.2654854 0.006194506 0.06586211 0.1520357

solution.w: 6.746e-07 7.592e-07 2.249e-07 0.9999782 3.526e-07 4.1014e-06 2.85e-08 1.0821e-06 9.611e-07 2.362e-07 9.118e-06 1.7245e-06 2.4999e-06

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0004174575

solution.v: 0.3320291 0.2218404 0.0465115 0.08822582 0.2353049 0.003702537 0.0002245648 0.0005923757 0.06895406 0.002614741

solution.w: 0.08499069 0.06651593 0.04737238 0.0570897 0.07026909 0.1536539 0.2174612 0.0437761 0.04602646 0.06367093 0.04062202 0.05176306 0.05675901

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 0.0001687669

solution.v: 4.5169e-06 4e-09 2.807e-07 1.155e-07 0.3887981 0.1581301 0.004566336 0.1895181 0.2498084 0.009173931

solution.w: 0.0430084 0.003839029 0.2186553 0.05830709 0.2614588 0.01310592 0.2028703 0.008094201 0.005122725 0.01229422 0.1636669 0.007318348 0.00225875

X1, X0, Z1, Z0 all come directly from dataprep object.

searching for synthetic control unit

MSPE (LOSS V): 1.961699e-05

solution.v: 0.004706325 0.00067057 0.0004105874 0.004706322 0.7520816 0.008783959 0.01856575 0.07940867 0.1225698 0.008096391

solution.w: 0.2314492 0.002820999 0.0007824972 0.05523455 0.0005929791 0.006473165 0.0001280334 0.01944595 0.6274088 0.0008995846 0.05107187 0.002142082 0.001550174

Exporting Final Stargazer Tables

% Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com % Date and time: Wed, Mar 05, 2025 - 10:46:57 AM % Table created by stargazer v.5.2.3 by Marek Hlavac, Social Policy Institute. E-mail: marek.hlavac at gmail.com % Date and time: Wed, Mar 05, 2025 - 10:47:00 AM

A9. Placebo Test for AOD Data

P.S.: Prepared for PhD Dissertation: Praharsh M. Patel {Ph.D.: Energy, Environmental, and Food Economics}